Quantifying Uncertainty in Void Swelling Prediction: A Conformal Prediction Framework for Reactor Safety Margins

TL;DR

This paper introduces a conformal prediction framework combined with ensemble machine learning models to provide calibrated uncertainty estimates for void swelling predictions in nuclear reactor materials, addressing stochastic damage effects.

Contribution

It presents a novel integration of conformal prediction with ensemble ML models to quantify uncertainty in void swelling predictions, considering physical heteroscedasticity.

Findings

Log-transformed conformal prediction achieves valid coverage in sparse data regimes.

The framework accurately captures the transition from incubation to steady-state swelling regimes.

Provides probabilistic risk assessment tools for reactor safety margins.

Abstract

Irradiation-induced void swelling is a critical degradation mechanism for structural materials in nuclear reactors, dictating component operational lifespan and safety. While recent machine learning (ML) approaches have improved the accuracy of swelling rate predictions, they often fail to account for the inherent stochasticity of radiation damage, providing point estimates without rigorous uncertainty quantification. This lack of probabilistic context limits their applications in materials qualification, reactor licensing and risk assessment. In this work, we develop a framework that integrates ensemble ML models with Conformal Prediction (CP) to generate statistically calibrated prediction intervals. Unlike standard error estimation or Bayesian methods that often rely on rigid distributional assumptions, this approach specifically addresses the physical heteroscedasticity of swelling data, where variance transitions from the nucleation-dominated incubation regime to the growth-dominated steady-state regime. We demonstrate that log-transformed conformal prediction inference provides valid empirical coverage consistent with target confidence levels even in sparse data regimes. This framework offers a pathway to replace overly conservative upper-bound curves with Probabilistic Risk Assessment (PRA) tools for high-dose reactor core internals.